Research Challenges in Applying Data Mining for Managing Energy Consumption, Finance and Social Inclusion Professor Sunil Vadera, University of Salford, uk

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Research Challenges in Applying Data Mining for Managing Energy Consumption, Finance and Social Inclusion

Professor Sunil Vadera, University of Salford, UK
Sunil Vadera is the Dean of the School of Computing, Science and Engineering at the University of Salford in Greater Manchester, UK. Sunil was Chair of the UK BCS Knowledge Discovery and Data Mining Symposium held in Salford in 2009, A Programme Chair of the IFIP conference on Intelligent Information Processing in 2010, 2012,2016. His research has been published in some of the leading outlets, including the Computer Journal, ACM Transactions on Knowledge Discovery from Data, ACM Computing Surveys, Expert Systems Journal, Foundations of Science, and IEEE Transactions of Power Systems. Sunil was Chair of the British Computer Society Academic Accreditations Committee, that has responsibility for professional accreditation of programmes in the UK, from 2007-2009. He holds a PhD in Computer Science from the University of Manchester, is a Fellow of the BCS and was awarded the BDO best British Indian Scientist and Engineer in 2014 in recognition of his contributions to the field.
Sunil Vadera has led a number of projects in applying data mining and machine learning for problems in Energy, Finance, and Policy over the last decade, including:

Developing new models for real time sensor validation of gas turbines
Data mining of near miss data for the health and safety executive
Analysis of SMART meters data for British Gas
A major FP7 funded project on Self-Learning Energy Efficient Buildings and Open Spaces
Analysing factors affecting children in need and troubled families
Sub-prime lending aimed at improving financial inclusion
Data mining for predicting client churn for IDOX Ltd, a major Software House

This presentation will give an overview of his experiences with some of these projects, highlight the lessons learned and outline the future challenges that need to be addressed if Big Data Analytics is going to be successful in addressing regional and global challenges such as managing energy consumption, climate change, finance, health and social inclusion.

Selected publications

Lomax, S. and Vadera, S. (2016). A Cost-Sensitive Decision Tree Learning Algorithm Based on a Multi-Armed Bandit Framework, The Computer Journal, doi:10.1093/comjnl/bxw015, available online at: http://comjnl.oxfordjournals.org/citmgr?gca=comjnl%3Bbxw015v1.

Nashnush, E., and Vadera, S. (2016). Learning cost-sensitive Bayesian networks via direct and indirect methods, Integrated Computer-Aided Engineering, doi: 10.3233/ICA-160514, available on line at: http://content.iospress.com/articles/integrated-computer-aided-engineering/ica514

Lomax, S. and Vadera, S.(2013). A survey of cost-sensitive decision tree induction algorithms, ACM Computing Surveys, Vol45, No 2, 35 pages

Sunil Vadera (2010), CSNL: A Cost-Sensitive Non-Linear Decision Tree Algorithm, ACM Transactions on Knowledge Discovery from Data, Vol 4, No 2, pp1-25

Ibarguengoyatia, P. Sucar, E. and Vadera, S. (2008). Sensor Validation, in Bayesian Networks, O. Pourret, P. Naim, P. and B. Marcot (Eds), Wiley, pp187- 202.

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